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"import MetaTrader5 as mt5\n",
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
"from datetime import datetime, timedelta\n",
"\n",
"# ----------------------------\n",
"# PARAMETERS\n",
"# ----------------------------\n",
"SYMBOL = \"BTCUSDc\"\n",
"TIMEFRAME = mt5.TIMEFRAME_D1\n",
"LOOKBACK_DAYS = 365 * 7 # 7 years\n",
"SEQ_LENGTH = 60\n",
"FEATURES = ['Close', 'pctB', 'RSI', 'MACD', 'Signal_Line', 'Momentum', 'ATR']\n",
"\n",
"# ----------------------------\n",
"# HELPER FUNCTIONS\n",
"# ----------------------------\n",
"def initialize_mt5():\n",
" if not mt5.initialize():\n",
" raise RuntimeError(f\"Failed to initialize MT5: {mt5.last_error()}\")\n",
"\n",
"def fetch_data(symbol, timeframe, lookback_days):\n",
" utc_now = datetime.utcnow()\n",
" from_date = utc_now - timedelta(days=lookback_days)\n",
" rates = mt5.copy_rates_range(symbol, timeframe, from_date, utc_now)\n",
" if rates is None or len(rates) == 0:\n",
" raise RuntimeError(f\"No data retrieved for {symbol}\")\n",
" df = pd.DataFrame(rates)\n",
" df['time'] = pd.to_datetime(df['time'], unit='s')\n",
" df.set_index('time', inplace=True)\n",
" return df\n",
"\n",
"def compute_indicators(df):\n",
" df['MA20'] = df['close'].rolling(20).mean()\n",
" df['MA50'] = df['close'].rolling(50).mean()\n",
" df['STD'] = df['close'].rolling(20).std()\n",
" df['Upper_Band'] = df['MA20'] + (df['STD'] * 2.5)\n",
" df['Lower_Band'] = df['MA20'] - (df['STD'] * 2.5)\n",
" df['pctB'] = (df['close'] - df['Lower_Band']) / (df['Upper_Band'] - df['Lower_Band'])\n",
"\n",
" delta = df['close'].diff()\n",
" up = delta.clip(lower=0)\n",
" down = -delta.clip(upper=0)\n",
" roll_up = up.rolling(14).mean()\n",
" roll_down = down.rolling(14).mean()\n",
" RS = roll_up / roll_down\n",
" df['RSI'] = 100.0 - (100.0 / (1.0 + RS))\n",
"\n",
" exp1 = df['close'].ewm(span=12, adjust=False).mean()\n",
" exp2 = df['close'].ewm(span=26, adjust=False).mean()\n",
" df['MACD'] = exp1 - exp2\n",
" df['Signal_Line'] = df['MACD'].ewm(span=9, adjust=False).mean()\n",
"\n",
" df['Momentum'] = df['close'] - df['close'].shift(10)\n",
" df['TR'] = df[['high','close']].max(axis=1) - df[['low','close']].min(axis=1)\n",
" df['ATR'] = df['TR'].rolling(14).mean()\n",
"\n",
" df.dropna(inplace=True)\n",
" return df\n",
"\n",
"def generate_signals(df):\n",
" # Simple backtest logic: Buy/Sell signals based on predicted vs RSI thresholds\n",
" df['Predicted_Change'] = df['close'].pct_change().shift(-1) # naive predictor: next close % change\n",
" rsi_buy = df['RSI'].quantile(0.4)\n",
" rsi_sell = df['RSI'].quantile(0.6)\n",
" pred_buy = df['Predicted_Change'].quantile(0.6)\n",
" pred_sell = df['Predicted_Change'].quantile(0.4)\n",
" df['Signal'] = 0\n",
" df.loc[(df['Predicted_Change'] > pred_buy) & (df['RSI'] < rsi_buy), 'Signal'] = 1\n",
" df.loc[(df['Predicted_Change'] < pred_sell) & (df['RSI'] > rsi_sell), 'Signal'] = -1\n",
" return df\n",
"\n",
"def backtest(df, initial_capital=500.0, transaction_cost=0.0005, stop_loss_pct=0.1, take_profit_pct=0.2):\n",
" cash = initial_capital\n",
" holdings = 0\n",
" entry_price = None\n",
" positions = []\n",
" portfolio_value = []\n",
"\n",
" for idx, row in df.iterrows():\n",
" price = row['close']\n",
" signal = row['Signal']\n",
"\n",
" # Enter long\n",
" if signal == 1 and cash > 0:\n",
" amount = cash * 0.5 * (1 - transaction_cost)\n",
" holdings += amount / price\n",
" cash -= amount\n",
" entry_price = price\n",
" positions.append({'Date': str(idx), 'Position': 'Buy', 'Price': price})\n",
"\n",
" # Exit long\n",
" elif signal == -1 and holdings > 0:\n",
" cash += holdings * price * (1 - transaction_cost)\n",
" holdings = 0\n",
" entry_price = None\n",
" positions.append({'Date': str(idx), 'Position': 'Sell', 'Price': price})\n",
"\n",
" # Stop loss / take profit\n",
" elif holdings > 0 and entry_price:\n",
" if price <= entry_price * (1 - stop_loss_pct):\n",
" cash += holdings * price * (1 - transaction_cost)\n",
" holdings = 0\n",
" entry_price = None\n",
" positions.append({'Date': str(idx), 'Position': 'Stop Loss', 'Price': price})\n",
" elif price >= entry_price * (1 + take_profit_pct):\n",
" cash += holdings * price * (1 - transaction_cost)\n",
" holdings = 0\n",
" entry_price = None\n",
" positions.append({'Date': str(idx), 'Position': 'Take Profit', 'Price': price})\n",
"\n",
" total_val = cash + holdings * price\n",
" portfolio_value.append(total_val)\n",
"\n",
" df['Portfolio_Value'] = portfolio_value\n",
" df['Daily_Return'] = df['Portfolio_Value'].pct_change()\n",
" df['Cumulative_Return'] = (1 + df['Daily_Return'].fillna(0)).cumprod()\n",
" total_return = (df['Portfolio_Value'].iloc[-1] - initial_capital) / initial_capital * 100\n",
" return df, positions, total_return\n",
"\n",
"# ----------------------------\n",
"# MAIN EXECUTION\n",
"# ----------------------------\n",
"if __name__ == \"__main__\":\n",
" initialize_mt5()\n",
" df = fetch_data(SYMBOL, TIMEFRAME, LOOKBACK_DAYS)\n",
" df = compute_indicators(df)\n",
" df = generate_signals(df)\n",
" df, positions, total_return = backtest(df)\n",
"\n",
" print(\"Backtest complete!\")\n",
" print(f\"Total return: {total_return:.2f}%\")\n",
" print(\"Positions executed:\")\n",
" for pos in positions[-5:]: # last 5 positions\n",
" print(pos)\n",
"\n",
" mt5.shutdown()\n"
]
}
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